ArticleCognitive neurodynamics2024
Inhibitory dynamics in dual-route evidence accumulation account for response time distributions from conflict tasks.
Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Ten years Diffusion Model for Conflict (DMC) tasks: Theoretical foundations, applications, practical recommendations, and open challenges.Psychonomic bulletin & review · 2026Review
- How visual eccentricity shapes conflict via target and distractor processing in the Simon task.Psychonomic bulletin & review · 2026Article
- The role of inhibition in modeling decision making with spiking neural networks.Frontiers in neuroinformatics · 2026Article
- A comment on the Revised Diffusion Model for Conflict tasks (RDMC).Psychonomic bulletin & review · 2025Review
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2 authors.
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Abstract
Laboratory data from conflict tasks, e.g. Simon and Eriksen tasks, reveal differences in response time distributions under different experimental conditions. Only recently have evidence accumulation models successfully reproduced these results, in particular the challenging delta plots with negative slopes. They accomplish this with explicit temporal dependencies in their structure or activation functions. In this work, we introduce an alternative approach to the modeling of decision-making in conflict tasks exclusively based on inhibitory dynamics within a dual-route architecture. We consider simultaneous automatic and controlled drift diffusion processes, with the latter inhibiting the former. Our proposed Dual-Route Evidence Accumulation Model (DREAM) achieves equivalent performance to previous works in fitting experimental response time distributions despite having no time-dependent functions. The model can reproduce conditional accuracy functions and delta plots with positive and negative slopes. The implications of these results, including an interpretation of the parameters and potential links to perceptual representations, are discussed. We provide Python code to fit DREAM to experimental data. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09990-8.
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